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“Waveglow: a flow-based generative network for speech synthesis,”
Open accessAbstract
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online [3].
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“When the sampling frequency (sampling rate) of the audio deepfakes is less than 16 kHz, perceived speech quality of audio deepfakes drops significantly, and the higher sampling rate may give way to higher audio quality (Prenger et al., 2019).”
From Audio deepfakes: A survey (2023) · cites this paper as Prenger et al., 2019
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Yang, L., Qin, D., & Zhang, J. (2021) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci52617.2021.9435882 · Cited by 8
Analysis and solution to aliasing artifacts in neural waveform generation models
Shang, Z., Zhang, H., Zhang, P., Wang, L., & Ta, L. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2022.109183 · Cited by 8
Kaneko, T., Kameoka, H., Tanaka, K., & Seki, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096288 · Cited by 8
FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis
Huang, R., Ren, Y., Jiang, Z. K., Cui, C., Liu, J., & Zhao, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.437 · Cited by 8
OverFlow: Putting flows on top of neural transducers for better TTS
Mehta, S., Kirkland, A., Lameris, H., Beskow, J., Székely, É., & Henter, G. E. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1996 · Cited by 8
Speech synthesis from intracranial stereotactic Electroencephalography using a neural vocoder
Arthur, F. V., & Csapó, T. G. (2024) · Híradástechnika/Infocommunications journal · doi:10.36244/icj.2024.1.6 · Cited by 9
Generative Time-series Modeling with Fourier Flows
Alaa, A. M., Chan, A., & van der Schaar, M. (2021) · International Conference on Learning Representations · Cited by 8
Shahriar, S., & Roken, N. A. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100066 · Cited by 8
ItôWave: Itô Stochastic Differential Equation is all You Need for Wave Generation
Wu, S., & Shi, Z. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746153 · Cited by 8
Restorable Image Operators with Quasi-Invertible Networks
Ouyang, H., Wang, T., & Chen, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20096 · Cited by 8
Ai, Y., Ling, Z.-H., Wu, W.-L., & Li, A. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3182268 · Cited by 8
Xu, S., Zhao, W., & Guo, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-349 · Cited by 8
HiFi-GAN based Text-to-Speech Synthesis in Serbian
Suzić, S., Pekar, D. J., Sečujski, M., Nosek, T., & Delić, V. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909548 · Cited by 8
Metadata
- Authors
- Prenger, R., Valle, R., & Catanzaro, B.
- Affiliations
- Nvidia (United States) (US)
- ORCID
- Catanzaro, Bryan
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 3617-3621
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2898847420
- Cited by
- 1,004
- References
- 22
- Citations by year
- 2026: 39 · 2025: 86 · 2024: 111 · 2023: 156 · 2022: 161 · 2021: 220 · 2020: 169 · 2019: 58
- FWCI
- 58.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- WaveGlow, speech synthesis, flow-based generative models, glow, WaveNet, audio generation, likelihood maximization, high-quality audio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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